Commercial and military wireless communication networks continue to be challenged by the increasingly dense and dynamic environments in which they operate. Modern commercial radios in these networks must receive, detect, extract, and successfully demodulate signals of interest (SOI's) to those radios in the presence of time and frequency coincident emissions from both fixed and mobile transmitters. These emissions can include both “multiple-access interference” (MAI), emitted from the same source or other sources in the radio's field of view (FoV), possessing characteristics that are nearly identical to the intended SOI's; and signals not of interest (SNOI's), emitted by sources unrelated to the intended SOI's, e.g., in unlicensed communication bands, or at edges of dissimilar networks, possessing characteristics that are completely different than those signals. In many cases, these signals can be quite dynamic in nature, both appearing and disappearing abruptly in the communications channel, and varying in their power level (e.g., due to power management protocols) and internal characteristics (e.g., transmission of special-purpose waveforms for synchronization, paging, or network acquisition purposes) over the course of a single transmission. The advent of machine-type communications (MTC) and machine-to-machine (M2M) communications for the Internet of Things (IoT) is expected to accelerate the dynamic nature of these transmissions, by increasing both the number of emitters in any received environment, and the burstiness of those emitters. Moreover, in groundbased radios and environments where the SOI or SNOI transmitters are received at low elevation angle, all of these emissions can be subject to dynamic, time-varying multipath that obscures or heavily distorts those emissions.
Radios in military communication networks encounter additional challenges that further compound these problems. In addition to multipath and unintended “benign” interference, these systems are also subject to intentional jamming designed to block communications between radios in the network. In many use scenarios, they may be operating in geographical regions where they must contend with strong emissions from host country networks. Lastly, these radios must impose complex transmission security (TRANSEC) and communications security (COMSEC) protocols on their transmissions, in order to protect the radios and connected network from corruption, cooption, or penetration by malicious actors.
The Mobile User Objective System (MUOS), developed to provide the next-generation of tactical U.S. military satellite communications, is an example of such a network. The MUOS network comprises a fleet of geosynchronous MUOS satellite vehicles (SV's), which connects ground, air, and seabased MUOS tactical radios to MUOS ground stations (“segments”) using “bent-pipe” transponders. The SV's receive signals from MUOS tactical radios over a 20 MHz (300-320 MHz) User-to-Base (U2B) band comprising four contiguous 5 MHz subbands, and transmit signals to MUOS tactical radios over a 20 MHz (360-380 MHz) “Base-to-User” (B2U) band comprising four contiguous 5 MHz subbands, using a physical layer (PHY) communication format based heavily on the commercial WCDMA standard (in which the MUOS SV acts as a WCDMA “Base” or “Node B” and the tactical radios act as “User Equipment”), with modifications to provide military-grade TRANSEC and COMSEC to those radios, and with a simplified common pilot channel (CPICH), provided for SV detection, B2U PHY synchronization, and network acquisition purposes, which is repeated continuously over 10 ms MUOS frames was so as to remove PHY signal components that could otherwise be selectively targeted by EA measures. Each MUOS satellite employs 16 “spot” beams covering different geographical regions of the Earth, which transmits a CPICH, control signals and information-bearing traffic signals to tactical radios in the same beam using CDMA B2U signals that are (nominally) orthogonal within each spot beam, i.e., which employ orthogonal spreading codes that allow complete removal of signals intended for other radios within that beam (in absence of multipath that may degrade that orthogonality); and which transmits CPICH, control signals, and traffic signals to radios in different beams using CDMA B2U signals and CPICH's that are nonorthogonal between spot beams, i.e., which employ nonorthogonal “Gold code” scrambling codes that provide imperfect separation of signals “leaking through” neighboring beams. In some network instantiations, multiple MUOS SV's may be visible to tactical radios and transmitting signals in the same B2U band or subbands, using nonorthogonal scrambling codes that provide imperfect separation of signals from those satellites. Hence, the MUOS network is subject to MAI from adjacent beams and SV's (Interference “Other Beam” and “Other Satellite”), as well as in-beam MAI in the presence of multipath (Interference “In-Beam”). See N. Butts, “MUOS Radio Management Algorithms,” in Proc. IEEE Military Comm. Conf., 2008, November 2008” (Butts2008) for a description of this interference. Moreover, the MUOS system is deployed in the same band as other emitters, including narrowband “legacy” tactical SatCom signals transmitted from previous generation networks, e.g., the UHF Follow-On (UFO) network, and is subject to both wideband co-channel interference (WBCCI) and narrowband CCI (NBCCI) from a variety of sources. See [E. Franke, “UHF SATCOM Downlink Interference for the Mobile Platform,” in Proc. 1996 IEEE Military Comm. Conf., Vol. 1, pp. 22-28, October 1996 (Franke 1996)] and [S. MacMullen, B. Strachan, “Interference on UHF SATCOM Channels,” in Proc. 1999 IEEE Military Comm. Conf., pp. 1141-1144, October 1999 (MacMullen 1999)] for a description of exemplary interferers. Lastly, the MUOS network is vulnerable to electronic attack (EA) measures of varying types, including jamming by strong WBCCI and spoofing by MUOS-like signals (also WBCCI), which may also be quite bursty in nature in order to elude detection by electronic countermeasures.
Developing hardware and software to receive, transmit, and above all make sense out of the intensifying ‘hash’ of radio signals received in these environments requires moving beyond the static and non-adaptive approaches implemented in prior generations of radio equipment. This requires the use of digital signal processing (DSP) methods that act on digital representations of analog received radio signals-in-space (SiS's), e.g., signals received by MUOS tactical radios, transformation between an analog representation and a digital representation thereof. Once in the digital domain, these signals can be operated on by sophisticated DSP algorithms that can detect, and demodulate SOI's contained within those signals at a precision that far exceeds the capabilities of analog processing. In particular, these algorithms can be used to excise even strong, dynamically varying CCI from those SOI's, at a precision that cannot be matched by fully or even partially analog interference excision systems (e.g., digitally-controlled analog systems).
For example, consider the environment described above, where a radio is receiving one or more SOI's in the presence of strong CCI, i.e., wideband SNOI's occupying the same band as those SOI's. Even SNOIs that are extremely strong (e.g. much stronger than any SOIs) can be removed from those received SOI's, by connecting the radio to multiple spatial or polarization diverse antenna feeds, e.g., multielement antenna arrays, that allow those SOI's and SNOI's to possess linearly-independent channel characteristics (e.g., strengths and phases) within the signals-in-space received on each feed, and using DSP which, by linearly combining (weighting and summing) those diverse feeds using diversity combiner weights that are preferentially calculated to substantively excise (cancel or remove) the SNOI's and maximize the power of each of the SOI's. This linear combining can be implemented using analog weighting and summing elements; however, such elements are costly and imprecise to implement in practice, as are the algorithms used to control those elements (especially if also implemented in analog form). This is especially true in scenarios where the interference is much stronger than the SOI's, requiring development of “null-steering” diversity combiners that must substantively remove the interferers without also substantively degrading the signal-to-noise ratio (SNR) of the SOI's. Moreover, analog linear combiners are typically only usable over wide bandwidths, e.g., MUOS bands or (at best) subbands, and can only separate as many SOI's and SNOI's as the number of receiver feeds in the system.
These limitations can be overcome by transforming the received signals-in-space from analog representation to digital representation, and then using digital signal processing to both precisely excise the CCI contained within those now-digital signals, e.g., using high-precision, digitally-implemented linear combiners, and to implementing methods for adapting those excision processors, e.g., to determine the weights used in those linear combiners. Moreover, the DSP based methods can allow simultaneous implementation of temporal processing methods, e.g., frequency channelization (analysis and synthesis filter banks) methods, to separately process narrowband CCI present in separate frequency bands, greatly increasing the number of interferers that can be excised by the system. DSP methods can react quickly to changes in the environment as interferers enter and leave the communication channel, or as the channel varies due to observed movement of the transmitter (e.g., MUOS SV), receiver, or interferers in the environment. Lastly, DSP methods facilitate the use of “blind” adaptation algorithms that can compute interference-excising or null-steering diversity weights without the need for detailed knowledge of the communication channel between the receiver and the SOI or SNOI transmitter (sometimes referred to as “channel state information,” or CSI). This capability can be extremely important if the radio is operating in the presence of heavy multipath that could obscure that CSI, eliminates the need for complex calibration procedures to learn and maintain array calibration data (sometimes referred to as “array manifold data”), or for addition or exploitation of complex and easily corruptible communication protocols to allow the receive to learn that CSI.
In the following embodiments, this invention describes methods for accomplishing such interference excision, to aid operation of a MUOS tactical radio operating in the presence of NBCCI and WBCCI. The MUOS tactical radio is assumed to possess a fully functional network receiver, able to detect and synchronize to an element of that network, e.g., a MUOS SV; and perform all operations needed to receive, demodulate, and additionally process (e.g., descramble, despread, decode, and decrypt) signals transmitted from that network element, e.g., MUOS B2U downlink transmissions. The radio is also assumed to possess a fully functional network transmitter that can perform all operations needed to transmit signals which that network element can itself receive, demodulate and additionally process, e.g., MUOS U2B signals intended for a MUOS SV. The radio is also assumed to be capable of performing all ancillary functions needed for communication with the network, e.g., network access, association, and authentication operations; exchange of PHY attributes such as B2U and U2B Gold code scrambling keys; exchange of PHY channelization code assignments needed for transmission of control and traffic information to/from the radio and network element; and exchange of encryption keys allowing implementation of TRANSEC and COMSEC measures during such communications. In addition, the radio and DICE appliqué are assumed to require no intercommunication to perform their respective functions. That is, the operation of the appliqué is completely transparent to the radio, and vice verse.
In these embodiments, the set of receive antennas (‘receive array’) can have arbitrary placement, polarization diversity, and element shaping, except that at least one receive antenna must have polarization and element shaping allowing reception of the signal received from the network element, e.g., it must be able to receive right-hand circularly polarized (RHCP) emissions in the 360-380 MHz MUOS B2U frequency band, and in the direction of the MUOS satellite. Additionally, the receive array should have sufficient spatial, polarization, and gain diversity to allow excision of interference also received by the receive array, such that it can achieve an signal-to-interference-and-noise ratio (SINR) that is high enough to allow the radio to despread and demodulate the receive array output signal. The antennas that form the receive array attached to the DICE system can be collocated with the system or radio, or can be physically removed from the system and/or connected through a switching or feed network; in particular, the location, physical placement, and characteristics of these antennas can be completely transparent or unknown to the system, except that they should allow the receive array to achieve an SINR high enough to allow the radio to demodulate the network receive signals.
The use of FPGA architecture allows hardware to be implemented which can adapt or change (within broader constraints that ASIC implementations) to match currently experienced conditions; and to identify transmitted components in, and transmitted features of, a SOI and/or SNOI. Particularly when evaluating diversity or multipath transmissions, identifying a received (observed) feature may be exploited to distinguish SOI from SNOI(s). The use of active beamforming can enable meaningful interpretation of the signal hash by letting the hardware actively extract only what it needs—what it is listening for, the signal of interest (SOI)—out of all the noise to which that hardware is exposed to and experiencing. One such development is the Dynamic Interference Cancellation and Excision (DICE) Appliqué. For such complex, and entirely reality-constrained, operational hardware and embedded processing firmware, DSP adaptation implementations of algorithms can best provide usable and sustainable transformative computations and constraints that enable both the transformation of the environmental hash into the ignored noise and meaningful signal subsets, and the exchange of meaningful signals.
In its embodiments, the invention will provide and transform the digital and analog representations of the signal between a radio (that receives and sends the analog radio transmissions) and the digital signal processing and analyzing elements (that manage and work with the digital representations of the signal). While separation of specialized hardware for handling the analog and digital representations is established in the industry[M] that is not true for exploitation of the 10 ms periodicity within the transformation and representation processes, which both improves computational efficiency and escapes problems arising from GPS antijam approaches in the prior art, used in the present invention.